Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined with real-time fMRI to propose a novel framework which turns on its head the conventional functional neuroimaging approach. This closed-loop method automatically designs the optimal experiment to evoke a desired target brain pattern. One of the challenges associated with extending such methods to real-time brain imaging is the need for adequate stopping criteria, an aspect of Bayesian optimization which has received limited attention. In light of high scanning costs and limited attentional capacities of subjects an accurate and reliable stopping criteria is essential. In order to address this issue we propose and empirically study the performance of two stopping criteria.

Written responses can provide a wealth of data in understanding student reasoning on a topic. Yet they are time- and labor-intensive to score, requiring many instructors to forego them except as limited parts of summative assessments at the end of a unit or course. Recent developments in Machine Learning (ML) have produced computational methods of scoring written responses for the presence or absence of specific concepts. Here, we compare the scores from one particular ML program -- EvoGrader -- to human scoring of responses to structurally- and content-similar questions that are distinct from the ones the program was trained on. We find that there is substantial inter-rater reliability between the human and ML scoring. However, sufficient systematic differences remain between the human and ML scoring that we advise only using the ML scoring for formative, rather than summative, assessment of student reasoning.

In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge. Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment--every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters.

See machine learning in action during "A Glimpse into the Future of Email Marketing – Reaping the Benefits of Machine Learning," featuring Kath Pay, Dela Quist, Skip Fidura and Jeremy Swift, May 19 at the Email Innovations Summit in Las Vegas. "Machine learning" has moved out of science fiction and into real-life applications, like powering Tesla cars that run on autopilot and robots that can beat humans at the Japanese game of Go. For marketers, it gets them closer to their email nirvana: true 1:1 personalization on a mass scale. Machine learning, at its simplest, is a method of data analysis that allows computers to learn – to analyze, predict and act – without explicit instructions or programming. That last phrase – "without explicit instructions or programming" – highlights the difference between today's rule-based marketing automation and systems that use machine learning.

You'll always have more fun with Grand Theft Auto when you prioritise chaos over common sense. So perhaps it's not such a surprise that watching an artificial deer'play' the game, with no purpose, has become an instant hit online. In essence, 'San Andreas Deer Cam' by digital artist Brent Watanabe is simple; instead of a human avatar or player, the game focuses instead on a single deer which is controlled by an artificial intelligence. With no direction from the artist, the deer wanders and trots around the 100 square miles of San Andreas, and interacts with its other AI inhabitants. What it surprising is how complex and oddly resonant the interactions of the deer turn out to be; in what is a testament to the AI skills of Rockstar as much as Watanabe, its adventures are peculiarly complex.

We all know that robots are already here, changing the way industries across the world work. The robotics industry is evolving at a fast pace. In addition to the more traditional industrial robots, there is an emerging demand for modern cobots which are intended to physically interact with humans in a shared workspace. One example of these modern cobots is ABB's YuMi, which was officially introduced to the marketplace at the end of 2015. YuMi is a "robotic co-worker" that will, according to the company, change the way we think about assembly automation.

Robotics is finally reaching the mainstream and androids - humanlike robots - are everywhere at SXSW Experts believe humanlike robots are the key to smoothing communication between humans and computers, and realizing a dream of compassionate robots that help invent the future of life. About CNBC: From'Wall Street' to'Main Street' to award winning original documentaries and Reality TV series, CNBC has you covered. Experience special sneak peeks of your favorite shows, exclusive video and more. Connect with CNBC News Online Get the latest news: http://www.cnbc.com/ Find CNBC News on Facebook: http://cnb.cx/LikeCNBC

First of all, though, there might still be things left to achieve with Go. Ke Jie, an 18-year-old Go virtuoso from China ranked #1 in the world, seemed cautiously optimistic about his own chances following Lee's first defeat last week, saying "it's 60 percent in favor of me." And many Go players have said they want to learn as much about AlphaGo as possible -- after all, it's only ever played a handful of games in public, demonstrating unorthodox, crushing tactics. It seems likely that AlphaGo will eventually be released to the public, and don't be surprised to see a match against Ke at some point; Lee Se-dol was chosen for his iconic stature and long career, but Ke is considered the stronger player today. DeepMind founder Demis Hassabis (above) has also said the company plans to test a version without any human training at all -- just the program teaching itself.

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